The present application relates to software and more specifically to user interface designs and methods for graphically displaying and interacting with data and/or concepts.
Software for facilitating information visualization, report generation, or analytic generation is employed in various demanding applications, including enterprise resource planning, scientific research, digital libraries, data mining, financial data analysis, market studies, manufacturing production control, drug discovery, and so on. Such applications often demand task-specific illustrative user interface display screens and accompanying visualizations for clearly illustrating data and accompanying characteristics, patterns, and interrelationships.
Space-efficient and illustrative visualizations are particularly important in enterprise applications, where data sets can vary widely, and where many types of visualizations may be available to render underlying data and provide associated software functionality. Visualizations for interpreting enterprise data may intuitively convey meaning inherent in underlying data and may include software functionality for exploiting the information to answer key business questions and to facilitate informed decision making.
Conventionally, software for visualization of enterprise data lacks effective mechanisms for facilitating custom visualization construction beyond specifying visualization source data and superficial visual encoding of rendered data attributes. For example, certain visualization software may include tools for facilitating creation of a pie chart from dimensions of a database table and may include user options for selecting colors for different slices of the pie chart.
Accordingly, conventional enterprise visualization software typically relies upon data characteristics to drive visualization creation, such that the resulting visualizations are tailored to the source data. However, users may not know what relationships they are seeking among data to be visualized, and consequently, may have difficulty selecting an appropriate visualization from a potentially large list of available visualizations.
Furthermore, users may become overwhelmed by potentially unnecessary or irrelevant source data, and users may not know what type of visualization to employ to render or otherwise display data. For example, a user may not know whether a pie chart, line graph, or tree diagram, sunburst visualization, or galaxy visualization would provide the most illustrative representation of the data for what the user is attempting to accomplish.
Hence, since users may not know what insight they seek or what questions they want answered, users often rely upon guesswork when selecting visualizations. However, such guesswork can inhibit informed decision making, resulting in potentially important decisions being made without critical insight.